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The Specification and Power of the Sign Test in Event Study Hypothesis Tests Using Daily Stock Returns

Published online by Cambridge University Press:  06 April 2009

Charles J. Corrado  and
Terry L. Zivney
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Abstract

This paper evaluates a nonparametric sign test for abnormal security price performance in event studies. The sign test statistic examined here does not require a symmetrical distribution of security excess returns for correct specification. Sign test performance is compared to a parametric t-test and a nonparametric rank test. Simulations with daily security return data show that the sign test is better specified under the null hypothesis and often more powerful under the alternative hypothesis than a t-test. The performance of the sign test is dominated by the performance of a rank test, however, indicating that the rank test is preferable to the sign test in obtaining nonparametric inferences concerning abnormal security price performance in event studies.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 27 , Issue 3 , September 1992 , pp. 465 - 478
Copyright
Copyright © School of Business Administration, University of Washington 1992

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References

Berry, M. A.; Gallinger, G. W.; and Henderson, G. V. Jr, “Using Daily Stock Returns in Event Studies and the Choice of Parametric Versus Nonparametric Test Statistics.” Quarterly Journal of Business and Economics, 29 (Winter 1990), 70–85.Google Scholar
Boehmer, E.; Musumeci, J.; and Poulsen, A. B.. “Event Study Methodology under Conditions of Event-Induced Variance.” Journal of Financial Economics, 30 (12 1991), 253–272.CrossRefGoogle Scholar
Brown, S., and Warner, J.. “Measuring Security Price Performance.” Journal of Financial Economics, 8 (03 1980), 205–258.CrossRefGoogle Scholar
Brown, S., and Warner, J.. “Using Daily Stock Returns: The Case of Event Studies.” Journal of Financial Economics, 14 (09 1985), 3–31.CrossRefGoogle Scholar
Corrado, C. J. “A Nonparemetric Test for Abnormal Security-Price Performance in Event Studies.” Journal of Financial Economics, 23 (08 1989), 385–395.CrossRefGoogle Scholar
Fama, E. F.Foundations of Finance. New York: Basic Books (1976).Google Scholar
Hettmansperger, T.Statistical Inference Based on Ranks. New York: Wiley (1984).Google Scholar
Jain, P. C. “Analyses of the Distribution of Security Market Model Prediction Errors for Daily Returns Data.” Journal of Accounting Research, 24 (Spring 1986), 76–96.CrossRefGoogle Scholar
Lehmann, E. L.Nonparametrics: Statistical Methods Based on Ranks. Oakland, CA: Holden-Day (1975).Google Scholar
Lehmann, E. L.Testing Statistical Hypotheses. New York: Wiley (1986).CrossRefGoogle Scholar
Maddala, G. S.Econometrics. New York: McGraw-Hill (1977).Google Scholar
Pearson, E. S., and Hartley, H. O.. Biometrika Tables for Statisticians, Vol. I. Cambridge, UK: Cambridge Univ. Press (1966).Google Scholar
Rohrbach, K., and Chandra, R.. “The Power of Beaver's U against a Variance Increase in Market Model Residuals.” Journal of Accounting Research, 27 (Spring 1989), 145–155.CrossRefGoogle Scholar
Sanders, R. W., and Robins, R. P.. “Discriminating between Wealth and Information Effects in Event Studies.” Review of Quantitative Finance and Accounting, 1 (08 1991), 307–330.CrossRefGoogle Scholar
Zivney, T. L., and Thompson, D. J. II “The Specification and Power of the Sign Test in Measuring Security Price Performance: Comments and Analysis.” The Financial Review, 24 (11 1989), 581–588.CrossRefGoogle Scholar